A machine learning framework and method for using the same

A machine learning framework and method for using the same

A machine learning framework and method for using the same are described. In one embodiment, the method for processing data with a machine learning framework comprises creating a plurality of features as independent features, each feature of the plurality features being based on one or more events t...

Ausführliche Beschreibung

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Bibliographische Detailangaben
Hauptverfasser: ABRAHAMAS, David, OSHEIM, Erik, TU, Susan, BOYKIN, Oscar, TIANSHI, Zhu, SWITZER, Tom
Format: Patent
Sprache:eng
Schlagworte:
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES
PHYSICS
SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR
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Beschreibung
Zusammenfassung:A machine learning framework and method for using the same are described. In one embodiment, the method for processing data with a machine learning framework comprises creating a plurality of features as independent features, each feature of the plurality features being based on one or more events that model a plurality of records related to payment processing information, creating a final feature that groups the plurality of features together, such that each feature of the plurality of features represents a sub-feature of the final feature, compiling the plurality of features and the final feature, computing, using a computing platform, each of the plurality of features as a separate job, including sending network related communications to access the payment processing information from one or more remote storage locations, and computing, using a computing platform, the final feature separately from computing the plurality of features, including grouping results of running each of the plurality of features together.